bigger context box

app.py

@@ -16,27 +16,29 @@ def predict(context="There are seven continents in the world.",question="How man
     print(f'predictions={predictions}')
     score = predictions['score']
     answer = predictions['answer']
-    return score,answer
+    start = predictions['start']
+    end = predictions['end']
+    return score,answer,start
 
 md = """
 If you came looking for chatGPT, sorry to disappoint, but this is different. This prediction model is designed to answer a question about a text. It is designed to do reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, accomplishing accurate reading comprehension can be a very valuable task, especially if you are attempting to get quick answers from a large (and maybe boring!) document.
 
+The model is based on the Zafrir et al. (2021) paper: [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) paper.
 Training dataset: SQuADv1.1, based on the Rajpurkar et al. (2016) paper: [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://aclanthology.org/D16-1264/)
 
-Based on the Zafrir et al. (2021) paper: [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) paper.
-
 """
 
 # predict()
-context=gr.Text(label="Context")
+context=gr.Text(lines=10,label="Context")
 question=gr.Text(label="Question")
 score=gr.Text(label="Score")
+start=gr.Text(label="Answer found at character")
 answer=gr.Text(label="Answer")
 
 iface = gr.Interface(
     fn=predict, 
     inputs=[context,question],
-    outputs=[score,answer],
+    outputs=[score,start,answer],
     title = "Question & Answer with Sparse BERT using the SQuAD dataset",
     description = md
     )